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Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing

arXiv 2101.00509 ↗First posted 2 Jan 2021cs.LG cs.AI cs.NEcited by 19

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Abstract (arXiv)

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in products. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated and compared based on a real industrial metal forming dataset.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 2 Jan 2021.

Builds on

10 of 21 references.

References · 10

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,718
  3. 2016Learning without Forgettingcited by 2,465
  4. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,313
  5. 2019Three scenarios for continual learningcited by 610
  6. 2018Progress & Compress: A scalable framework for continual learningcited by 516
  7. 2018Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselinescited by 228
  8. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  9. 2018Continuous Learning in Single-Incremental-Task Scenarioscited by 109
  10. 2020Deep Transfer Learning for Industrial Automation: A Review and Discussion of New Techniques for Data-Driven Machine Learningcited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 4; 2023: 3; 2024: 4; 2025: 3; 2026: 1.

19 papers.

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  1. 2021A Survey on Anomaly Detection for Technical Systems using LSTM Networkscited by 27
  2. 2023A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directionscited by 22
  3. 2022A Theory for Knowledge Transfer in Continual Learningcited by 16
  4. 2024Unveiling the Anomalies in an Ever-Changing World: A Benchmark for Pixel-Level Anomaly Detection in Continual Learningcited by 13
  5. 2022Continual Learning Approaches for Anomaly Detectioncited by 9
  6. 2021Regularization-based Continual Learning for Fault Prediction in Lithium-Ion Batteriescited by 5
  7. 2024Overcoming Catastrophic Forgetting in Tabular Data Classification: A Pseudorehearsal-based approachcited by 5
  8. 2021Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Datacited by 4
  9. 2026Contrastive Continual Learning for Model Adaptability in Internet of Thingscited by 4
  10. 2022Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learningcited by 2
  11. 2025Boosting-inspired online learning with transfer for railway maintenancecited by 2
  12. 2021Transfer Learning as an Enhancement for Reconfiguration Management of Cyber-Physical Production Systemscited by 1
7 more
  1. 2023Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturingcited by 1
  2. 2023Continual learning for surface defect segmentation by subnetwork creation and selectioncited by 1
  3. 2025Incremental Learning with Repetition via Pseudo-Feature Projectioncited by 1
  4. 2022Towards Deep Industrial Transfer Learning: Clustering for Transfer Case Selectioncited by 0
  5. 2024Slowing Down Forgetting in Continual Learningcited by 0
  6. 2024ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecastingcited by 0
  7. 2025Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Weldingcited by 0